which utilizes the dependency between tasks and meanwhile reduces the train-test discrepancy. In the period of maximum likelihood estimation (MLE), we use the Gumbel-softmax approach [1]} when sampling \(\widetilde{m} \sim p(m | R)\)  which can back-propagate gradients between tasks (details can be found in Appendix ).
